所有值的95％的范围实际上代表中间的95％值。因此，我们可以找到第2.5个百分点和第97.5个百分点，从而可以获得95％的中间范围。为此，我们可以在R中使用分位数功能。要找到第2.5个百分位数，我们将需要使用概率= 0.025，而对于第97.5个百分位数，我们可以使用概率= 0.0975。
x1<-1:10 x1
输出结果
[1] 1 2 3 4 5 6 7 8 9 10
quantile(x1,probs=c(0.025,0.975))
输出结果
2.5% 97.5% 1.225 9.775
x2<-sample(0:9,200,replace=TRUE) x2
输出结果
[1] 8 1 5 4 7 9 7 4 3 9 3 0 5 4 4 3 8 2 7 7 4 0 1 8 2 1 0 2 6 3 3 5 7 0 9 6 9 [38] 1 3 2 7 9 2 3 9 0 5 2 7 6 6 2 4 5 1 6 6 7 9 8 5 7 5 4 8 4 3 3 8 6 1 1 1 9 [75] 6 2 0 9 8 0 2 2 2 6 2 8 8 7 4 5 1 0 1 3 7 9 5 4 5 2 5 5 4 7 5 4 5 6 6 2 9 [112] 6 9 2 7 1 5 0 1 4 7 0 8 8 2 5 4 9 4 8 4 0 7 2 1 7 0 6 5 2 5 6 3 2 1 5 6 6 [149] 6 0 4 1 8 7 1 5 0 1 8 9 8 6 8 7 6 8 4 3 9 3 9 2 0 3 8 3 7 8 9 6 4 3 4 5 6 [186] 4 1 6 0 5 8 1 5 1 3 7 2 3 3 3
> quantile(x2,probs=c(0.025,0.975))
输出结果
2.5% 97.5% 0 9
x3<-sample(1:100,200,replace=TRUE) x3
输出结果
[1] 14 49 72 45 19 80 43 88 73 100 83 55 10 50 71 69 47 22 [19] 15 99 30 2 51 89 69 66 87 25 59 34 77 40 40 44 41 5 [37] 75 35 33 40 7 40 64 17 79 77 27 49 8 20 30 29 15 67 [55] 36 18 53 80 57 71 96 40 12 92 94 87 14 17 43 73 90 28 [73] 44 41 47 44 57 23 54 88 64 26 33 80 44 9 2 49 36 40 [91] 38 74 48 49 75 83 71 55 8 99 32 8 89 23 62 86 4 14 [109] 33 30 1 77 73 3 66 90 39 84 73 25 45 74 33 97 46 82 [127] 68 6 18 43 20 76 42 69 52 98 14 27 13 62 33 65 16 100 [145] 9 5 22 29 3 30 91 63 25 86 71 75 36 85 56 80 42 89 [163] 56 44 16 23 94 13 14 89 83 100 40 94 36 85 74 57 77 95 [181] 23 84 53 1 48 62 92 27 8 32 63 52 99 10 12 71 59 64 [199] 42 54
quantile(x3,probs=c(0.025,0.975))
输出结果
2.5% 97.5% 3 99
x4<-sample(100:999,200) x4
输出结果
[1] 820 234 148 100 865 811 694 864 197 140 815 588 158 521 542 115 675 932 [19] 169 494 257 549 963 340 595 814 324 182 952 291 936 601 743 794 610 377 [37] 495 179 284 739 484 901 627 376 609 220 784 418 721 488 738 944 712 458 [55] 551 166 138 857 801 785 500 722 989 394 517 640 238 688 485 426 637 133 [73] 881 504 625 809 445 916 200 802 306 955 581 937 466 639 247 502 146 740 [91] 413 655 767 996 886 935 723 361 286 131 269 167 186 959 396 805 665 218 [109] 696 883 253 454 400 949 175 777 758 971 691 395 603 435 934 406 843 281 [127] 553 976 267 888 127 913 178 987 787 354 290 977 225 539 995 803 419 288 [145] 221 294 550 442 525 782 366 586 501 875 567 543 828 451 830 821 992 342 [163] 737 611 806 753 876 487 449 313 719 578 983 160 768 556 933 680 956 375 [181] 761 678 279 398 755 139 330 686 824 321 819 335 580 674 348 671 246 509 [199] 447 499
quantile(x4,probs=c(0.025,0.975))
输出结果
2.5% 97.5% 137.875 983.100
x5<-rnorm(80,5,2) x5
输出结果
[1] 6.98972600 3.17248565 5.13234480 4.11901047 7.77081721 4.70660218 [7] 4.34543482 5.32969562 3.85105871 4.92334515 10.38088424 2.78494280 [13] 2.17723638 5.89778299 3.25433020 5.76595115 6.58821842 2.16789176 [19] 6.70022121 4.33298204 2.52347763 7.17797129 5.96444881 -0.49521879 [25] 3.32652489 6.36352461 7.02582094 4.76756040 0.05690139 3.60584007 [31] 6.43240996 1.91232232 4.03257916 9.08311081 8.88715843 7.76594592 [37] 5.80933391 3.37731011 3.14555718 6.14552974 5.65748181 2.88725211 [43] 6.81291913 5.28996898 6.84361973 4.71988891 3.13190129 3.45525499 [49] 6.02927444 4.99564376 7.46594963 0.70237604 5.29670524 4.31319790 [55] 4.31986763 2.19272850 4.03273654 6.97718448 4.50745487 4.36807171 [61] 7.39283829 2.14748082 4.48435806 4.51189441 5.35723362 8.93982200 [67] 6.83182644 4.27771018 7.26854753 3.78881429 4.19227762 4.24381458 [73] 2.93295093 7.40268495 3.35842472 3.73021960 5.85442187 7.90967270 [79] 2.90778517 6.01023427
quantile(x5,probs=c(0.025,0.975))
输出结果
2.5% 97.5% 0.6862392 8.9434042
x6<-rnorm(50) x6
输出结果
[1] 0.3331076074 0.6607651759 0.0240865785 -0.1448891552 -0.6969449129 [6] 0.0166860867 -1.2130240135 -0.9468299995 0.7802634147 0.9585927355 [11] -1.6272935094 1.3593188263 -0.0935888333 2.0885770523 -0.4199458516 [16] -0.0089055888 -0.1935638804 -1.6772774160 -2.0102456085 0.0009071378 [21] 1.9596100041 0.3502070761 0.6889992063 0.9485294768 0.4670174843 [26] 0.4977238683 -0.2571625431 -0.2327554066 -0.7488511452 0.7068129587 [31] 0.6054530742 1.6431915914 0.8637796961 -0.3100868562 1.9966920903 [36] 0.1412141929 0.3438465534 -0.3179982685 0.5358806944 1.9328734967 [41] -0.4732611071 -0.3964032732 -0.6116624673 -0.4275941636 0.3976625756 [46] 1.1928187412 0.5876758446 0.2999995481 0.3585979367 0.2490096352
quantile(x6,probs=c(0.025,0.975))
输出结果
2.5% 97.5% -1.666031 1.988349
x7<-rpois(120,5) x7
输出结果
[1] 5 4 8 6 7 6 2 7 6 1 8 6 6 1 6 7 5 2 7 3 8 5 6 6 7 [26] 1 5 8 6 0 1 1 4 5 5 7 4 2 5 6 4 6 13 12 4 5 4 3 3 2 [51] 5 6 4 5 3 8 2 2 6 4 3 4 4 9 5 1 5 5 5 7 9 2 1 6 2 [76] 5 6 7 3 7 9 4 2 3 3 1 5 3 5 7 10 7 3 4 2 4 7 3 3 5 [101] 6 4 4 3 4 2 6 3 5 6 2 9 2 2 2 6 5 3 7 3
quantile(x7,probs=c(0.025,0.975))
输出结果
2.5% 97.5% 1.000 9.025